1. Field
The invention relates to the field of analytical methods suitable for diagnosing diseases with human or animal samples. To analyze the samples, electromagnetic spectra are recorded, stored and analyzed by multivariate analytical methods. The diagnosis of diseases using multivariate analytical methods is already known in the prior art and is explained in greater detail below. However, with the help of the inventive method, it is also possible for the first time to reliably detect individual manifestations of a disease syndrome in addition to detecting a specific disease. The inventive method thus makes it possible for the first time to diagnose individual stages of progression of a disease; this could provide information regarding the development of a disease, for example, or might reflect the effects of therapeutic methods on a patient's health status.
2. Description of the Related Art
In the prior art, most diagnostic methods are currently based on detection of individual characteristic substances in a sample to be analyzed to obtain information regarding a disease. These substances are detected in blood, urine or other body fluids, for example. Substances characteristic of a disease include, for example, disease-induced metabolites which accumulate in the body and can be detected in the blood. Samples taken from patients are analyzed by clinical analytical methods or by spectroscopic methods and are correlated with the patient's disease symptoms. On the basis of these findings, a medical diagnosis is made, taking into account the analytical results obtained on the sample as well as the patient's disease symptoms.
Frequently, however, it is found that diagnosing individual diseases is particularly difficult because the substances to be analyzed for the disease cannot be determined unambiguously or are even unknown. A diagnosis of such diseases can often be ensured only through complex analyses of varied parameters which provide an indication of the disease. For example, the so-called metabolic syndrome is an example of a disease that is difficult to diagnose; it is assumed that at least one of ten adults in the United States suffers from this syndrome. This disease is thus one of the most common health anomalies in western civilization. Metabolic syndrome involves an accumulation of five different risk factors for having a high cardiovascular mortality. Metabolic syndrome is diagnosed when a patient has at least three of five risk factors. The risk factors, i.e., parameters for detection of this disease, include obesity, hypertension, elevated cholesterol levels, elevated blood sugar and blood lipid levels. The example given here shows that five different tests are necessary to allow this disease to be diagnosed.
To simplify such complex test methods and gain a greater diagnostic certainty, methods of disease analysis have been developed that allow a reliable diagnosis to be made with just one measurement method even without a knowledge of a definite substance characteristic of a disease. These methods are performed by IR spectroscopy, for example, and utilize multivariate analytical methods, among others. With the help of multivariate analytical algorithms, it is possible thereby to perform a pattern recognition on spectra without having to perform a characterization of individual substances. Multivariate analytical methods make it possible to analyze an unknown sample with a high reliability and correlate it with various diseases as a function of a previously trained database. To compile the database, samples are first taken from patients having a specific syndrome. The measured spectra are then allocated to a class characterizing the particular disease family. If infrared spectra are recorded on a plurality of samples or methods belonging to a known class according to identical or at least comparable measurement methods, then there may be training of the database. These spectra are supplied to the multivariate analytical method according to their classification so that the database includes characteristic spectra patterns, each belonging to a certain class and thus belonging to a disease family. The system is trained with the help of multivariate analytical algorithms until a distinction can be made between different classes. For example, in this way it is possible to categorize samples taken from healthy people or from people sick with an anomalous condition that is to be investigated. If an unknown sample is measured, the system is able to correlate the unknown sample with a class with sufficient reliability so that a diagnostic indication is given. This is preferably based on pattern recognition of the measured spectrum in a previously defined measured wavelength range, and an allocation of an unknown sample to a previously classified disease group (sick/healthy) is then made on this basis. The basic principles of such a method which permits classification of samples by IR spectroscopy and multivariate analytical methods are described in the document EP 0 644 412, for example.
In addition, use of such a method for analyzing metabolic syndrome is disclosed in the document WO 03060515 A1. To this end, spectra of healthy patients and spectra of patients suffering from metabolic syndrome are taken in a predetermined wavelength range, stored and classified in a database.
Depending on the analysis to be performed, a wavelength range in which characteristic spectra patterns permit a reliable allocation to one class is selected. For example, a determination of the particular wavelength range which has proven suitable for analyzing a certain disease syndrome can be done, e.g., by means of multivariate analyses or by algorithms such as those described in NMA Biomedicine, vol. 11, issue 4-5 (1998), pp. 209-216. Consequently, a correlation matrix is first set up in various analytical methods such as discriminance analysis and cluster analysis. The analytical method then determines which wavelengths are to be used with which influence for classification on the basis of the correlation matrix. The wavelengths used for analysis span a space that matches in dimension the number of wavelengths. A subspace in which the allocation of samples to classes can be performed unambiguously is then selected by the analytical method. This results in an allocation of samples to a class from their spatial proximity in said subspace. As soon as a scheme for classification according to a disease family has been developed, the data can be stored in the system, so that an analytical system can make suggested diagnoses for a plurality of diseases.
Multivariate analytical methods such as those known in the prior art are explained in greater detail below on the basis of the example of discriminance analysis.
Linear discriminance analysis permits a separation between two data records, which in the example cited here reflect, for example, patient data on a healthy patient and on a sick patient, with the goal of permitting a reliable differentiation between the data records. Practice has shown that there is often overlapping of data records, with a number of data possibly being allocated both to the “healthy” data record and also to the “sick” data record. There is thus no definite classification of this data, which may be referred to as the intersection of the “healthy” and “sick” data records in the example cited here. An allocation of unknown samples whose measured parameters are in this intersection range of the two data records is therefore impossible. Like other multivariate analytical methods, linear discriminance analysis draws a boundary between two data records to which an allocation is to be made. All data within this limit/these limit values belongs to the “healthy class,” for example. The larger the data record of a class, the more sensitive is an allocation of an unknown sample in this class, so that almost all samples belonging to this class are also allocated to it. However, such a large data record also includes the allocation of samples that have been incorrectly allocated to this class so that a sensitive allocation will have a low specificity accordingly. Therefore, the object of such analytical methods is to achieve an optimized allocation, finding a balance between sensitivity and specificity. Training in the program is possible, increasing the sensitivity and specificity of the allocation by measurement and allocation of known samples. In practice, in the analysis the process parameters that characterize the sensitivity and specificity are cited according to the respective allocation. The values of unknown samples which are directly in an intersection of data records are evaluated as not analyzable in order to avoid an excessive error in the allocation. A diagnostic indication cannot be given for a sample whose parameters are within the intersection.
Consequently, one disadvantage of the prior art is that a disease can be diagnosed only with respect to different classes (e.g., “healthy” or “sick”), without being able to take into account data within the intersection, for example. Thus the course of a disease in a patient, which is continuous per se, is divided into two discrete steps which allow only a “healthy” or “diseased” condition as a diagnostic indication. It is thus impossible to take into account a real course of a disease having stages of the disease.